Forem: C.C.

The Ultimate OpenCode.ai Setup: Why I’m using Vivgrid’s GPT-5.1-Codex

C.C. — Mon, 05 Jan 2026 02:59:17 +0000

OpenCode

OpenCode.ai is an open-source, model-agnostic AI coding agent designed to bring powerful AI assistance directly into developers’ workflows—whether you are working in the terminal, using a desktop application, or inside an IDE extension.

Unlike traditional autocomplete tools, OpenCode operates as an interactive coding agent. It understands project context, reasons across files, refactors existing code, fixes bugs, generates new features, and helps developers explore large codebases using natural-language instructions.

Key features include:

Terminal-first, multi-interface design A native terminal UI (TUI), with optional desktop and IDE extensions, allows developers to work in their preferred environment.
Model-agnostic architecture OpenCode can connect to 75+ LLM providers (cloud, on-premise, or local) via Models.dev and the AI SDK.
Context-aware intelligence Language Server Protocol (LSP) integration enables deep understanding of project structure and symbols.
Multi-session collaboration Run parallel agent sessions within the same repository and share them for collaborative debugging or review.
Privacy-first by design Code and project context are not stored on remote servers, making OpenCode suitable for sensitive or proprietary codebases.

Configure OpenCode.ai to Use Vivgrid (GPT-5.1-Codex)

One of OpenCode’s core strengths is its clean separation between authentication and runtime model configuration. Credentials are configured independently from model selection, allowing you to switch providers or models without changing your workflow.

To use Vivgrid as the provider and gpt-5.1-codex as the model, you only need to configure two local files.

1. Configure Authentication

File: ~/.local/share/opencode/auth.json

This file stores API credentials locally. OpenCode does not upload this file; it is read only by the local CLI / TUI.

{
  "vivgrid": {
    "type": "api",
    "key": "VIVGRID_API_KEY_HERE"
  }
}

Notes:

vivgrid is the provider name referenced later in opencode.json
This file is used for credential storage only
Treat the API key as a secret and never commit it to version control

2. Configure OpenCode Runtime

~/.config/opencode/opencode.json

This file defines which provider and model OpenCode actually uses.

Minimal working example:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "vivgrid": {
      "npm": "@ai-sdk/openai",
      "name": "Vivgrid",
      "options": {
        "baseURL": "https://api.vivgrid.com/v1"
      },
      "models":{
        "gpt-5.1-codex": {
          "name": "gpt-5.1-codex",
          "options": {
            "reasoningEffort": "high",
            "textVerbosity": "medium",
            "reasoningSummary": "auto"
          }
        }
      }
    }
  },
  "model": "vivgrid/gpt-5.1-codex"
}

3. Verify the Setup

Run a simple command in any project directory:

opencode run "hello world"

If the configuration is correct, OpenCode will route the request through Vivgrid and execute it using gpt-5.1-codex.

4. Debugging

If you encounter issues, enable detailed logs:

opencode run "hello" --print-logs --log-level DEBUG

This will surface provider initialization, model resolution, and API-level errors.

5. GUI

This configuration also works for OpenCode GUI App.

Summary

With only two small configuration files, OpenCode becomes a provider-agnostic AI coding agent, while Vivgrid serves as a drop-in, OpenAI-compatible backend for GPT-5.1-Codex.

This clear separation between credential management and runtime configuration is what makes OpenCode especially well-suited for advanced models, custom providers, and production-grade AI coding workflows.

Streaming Image Recognition by WebAssembly and Tensorflow

C.C. — Sun, 27 Jun 2021 08:03:24 +0000

This project demonstrates how to process a video stream in real-time using WebAssembly and apply a pre-trained food classification model to each frame of the video in order to determine if food is present in that frame, all by integrating WasmEdge into YoMo serverless.

Advantages:

⚡️ Low-latency: Streaming data processing applications can now be done in far edge data centers thanks to YoMo's highly efficient network services
🔐 Security: WasmEdge runs the data processing function in a WebAssembly sandbox for isolation, safety, and hot deployment
🚀 High Performance: Compared with popular containers, such as Docker, WasmEdge can be up to 100x faster at startup and have a much smaller footprint
🎯 Edge Devices: As WasmEdge consumes much less resources than Docker, it is now possible to run data processing applications on edge devices

Open-source projects that we used:

Serverless stream processing framework YoMo
Integrate with WasmEdge to introduce WebAssembly, interop TensorFlow Lite model
A deep learning model found on TensorFlow Hub; make sure to download TFLite (aiy/vision/classifier/food_V1), which was created by Google

Steps to run

1. Clone This Repository

$ git clone https://github.com/yomorun/yomo-wasmedge-tensorflow.git

2. Install YoMo CLI

$ go install github.com/yomorun/cli/yomo@latest
$ yomo version
YoMo CLI version: v0.0.5

Or, you can download the pre-built binary tarball yomo-v0.0.5-x86_64-linux.tgz.

Details about YoMo CLI installation can be found here.

3. Install WasmEdge Dependencies

Install WasmEdge

$ wget https://github.com/WasmEdge/WasmEdge/releases/download/0.8.0/WasmEdge-0.8.0-manylinux2014_x86_64.tar.gz
$ tar -xzf WasmEdge-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo cp WasmEdge-0.8.0-Linux/include/wasmedge.h /usr/local/include
$ sudo cp WasmEdge-0.8.0-Linux/lib64/libwasmedge_c.so /usr/local/lib
$ sudo ldconfig

Or, you can build from the source.

Install WasmEdge-tensorflow

Install tensorflow dependencies for manylinux2014 platform

$ wget https://github.com/second-state/WasmEdge-tensorflow-deps/releases/download/0.8.0/WasmEdge-tensorflow-deps-TF-0.8.0-manylinux2014_x86_64.tar.gz
$ wget https://github.com/second-state/WasmEdge-tensorflow-deps/releases/download/0.8.0/WasmEdge-tensorflow-deps-TFLite-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo tar -C /usr/local/lib -xzf WasmEdge-tensorflow-deps-TF-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo tar -C /usr/local/lib -xzf WasmEdge-tensorflow-deps-TFLite-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo ln -sf libtensorflow.so.2.4.0 /usr/local/lib/libtensorflow.so.2
$ sudo ln -sf libtensorflow.so.2 /usr/local/lib/libtensorflow.so
$ sudo ln -sf libtensorflow_framework.so.2.4.0 /usr/local/lib/libtensorflow_framework.so.2
$ sudo ln -sf libtensorflow_framework.so.2 /usr/local/lib/libtensorflow_framework.so
$ sudo ldconfig

Install WasmEdge-tensorflow:

$ wget https://github.com/second-state/WasmEdge-tensorflow/releases/download/0.8.0/WasmEdge-tensorflow-0.8.0-manylinux2014_x86_64.tar.gz
$ wget https://github.com/second-state/WasmEdge-tensorflow/releases/download/0.8.0/WasmEdge-tensorflowlite-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo tar -C /usr/local/ -xzf WasmEdge-tensorflow-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo tar -C /usr/local/ -xzf WasmEdge-tensorflowlite-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo ldconfig

Install WasmEdge-image:

$ wget https://github.com/second-state/WasmEdge-image/releases/download/0.8.0/WasmEdge-image-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo tar -C /usr/local/ -xzf WasmEdge-image-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo ldconfig

If you have any questions about installation, please refer to the official documentation. Currently, this project works on Linux machines only.

Install video and image processing dependencies

$ sudo apt-get update
$ sudo apt-get install -y ffmpeg libjpeg-dev libpng-dev

4. Write your Streaming Serverless function

Write app.go to integrate WasmEdge-tensorflow:

Get WasmEdge-go:

$ cd flow
$ go get -u github.com/second-state/WasmEdge-go/wasmedge

Download pre-trained TensorflowLitee model: lite-model_aiy_vision_classifier_food_V1_1.tflite, store to rust_mobilenet_foods/src:

$ wget 'https://storage.googleapis.com/tfhub-lite-models/google/lite-model/aiy/vision/classifier/food_V1/1.tflite' -O ./rust_mobilenet_food/src/lite-model_aiy_vision_classifier_food_V1_1.tflite

Compile to wasm file:

Install rustc and cargo

$ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
$ export PATH=$PATH:$HOME/.cargo/bin
$ rustc --version

Set default rust version to 1.50.0: $ rustup default 1.50.0

Install rustwasmc

$ curl https://raw.githubusercontent.com/second-state/rustwasmc/master/installer/init.sh -sSf | sh
$ cd rust_mobilenet_food
$ rustwasmc build
# The output WASM will be `pkg/rust_mobilenet_food_lib_bg.wasm`.

Copy pkg/rust_mobilenet_food_lib_bg.wasm to flow directory:

$ cp pkg/rust_mobilenet_food_lib_bg.wasm ../.

5. Run YoMo Orchestrator Server

  $ yomo serve -c ./zipper/workflow.yaml

6. Run Streaming Serverless function

$ cd flow
$ go run --tags "tensorflow image" app.go

7. Demonstrate video stream

Download this demo vide: hot-dog.mp4, store to source directory, then run：

$ wget -P source 'https://github.com/yomorun/yomo-wasmedge-tensorflow/releases/download/v0.1.0/hot-dog.mp4'
$ go run ./source/main.go ./source/hot-dog.mp4

8. Result

DEBS Grand Challenge: Real-Time Stream Processing with YoMo

C.C. — Thu, 13 May 2021 16:10:18 +0000

Introduction

DEBS or the ACM International Conference on Distributed Event Based Systems aims to "provide a forum dedicated to the dissemination of original research, the discussion of practical insights, and the reporting of experiences relevant to event based computing that were previously scattered across several scientific and professional communities" (reference).

The DEBS 2014 Grand Challenge - Smart Grid is the 8th ACM International Conference on Distributed Event Based Systems, focused on two problems which are relevant to the industry: real-time load prediction and anomaly detection. The data for the challenge was collected from a number of smart-home installations deployed in Germany.

In traditional processing, data is often stored in the database at first, and then processed to obtain useful information at a secondary stage. The traditional architecture:

However, with stream processing, we're swiftly able to run real-time analytics on incoming data streams. By utilizing Yomo, an open-source serverless stream processing framework, we can address DEBS' challenge in a real-time fashion:

Firstly, we will take a look at the type of data we're dealing with. Next, we will introduce two queries that were originally proposed by ACM DEBS. Lastly, we install YoMo (an open-source framework for real-time stream processing) to implement both queries as described.

Data

From Jerzak and Ziekow (2014):

For the DEBS 2014 Grand Challenge we assume a hierarchical structure with a house, identified by a unique house id, being the topmost entity. Every house contains one or more households, identified by a unique household id (within a house). Every household contains one or more smart plugs, each identified by a unique plug id (within a household). Every smart plug contains exactly two sensors:
(1) load sensor measuring current load with Watt as unit (2) work sensor measuring total accumulated work since the start (or reset) of the sensor with kWh as unit.

The input stream is defined as follows:

id – a unique identifier of the measurement [32 bit unsigned int]
timestamp – timestamp of measurement [32 bit unsigned int]
value – the measurement [32 bit float]
property – type of the measurement: 0 for work or 1 for load [boolean]
plug_id – a unique identifier (within a household) of the smart plug [32 bit unsigned int]
household_id – a unique identifier of a household (within a house) where the plug is located [32 bit unsigned int]
house_id – a unique identifier of a house where the household with the plug is located [32 bit unsigned int]

The complete data file is available under this link. For demonstration purposes, we will generate mock data using this file. In real life, we should be dealing with direct sensor data.

Queries

Load Prediction

Let's divide the whole period from t_{start} = 1377986401 to t_{end} = 1380578399 covered by the dataset into N equal slices of |s| seconds and call them s_0, s_1, s_2, etc.

The average load for slice s_{i + 2} is given by

L(s_{i + 2}) = ( avgLoad(s_i) + median({ avgLoad(s_j) }) ) / 2

s_j is a nonempty set defined by s_(i + 2 – n * k), where k is the number of slices within a 24-hour period and n is a natural number between 1 and floor((i + 2) / k).

Outliers

For this query, we will compute the percentage of plugs that have a median load during the last hour greater than the median load of all plugs.

YoMo – what is it and why do we use it?

YoMo is an open-source serverless streaming framework for building low-latency edge computing applications. Built atop QUIC transport protocol and functional reactive programming interface, it makes real-time data processing reliable, secure, and easy.

Getting Started

Follow the instructions here to install YoMo. Assuming that $GOPATH has been set on your device, you should be able to see a directory with the name $GOPATH/src/github.com/yomorun/yomo.
Run the following commands to create a new project. Don't forget to replace ${YOUR_GITHUB_USERNAME} with your actual GitHub username!

$ mkdir -p $GOPATH/src/github.com/${YOUR_GITHUB_USERNAME} && cd $_
$ yomo init debs-flow

Now, cd into $GOPATH/src/github.com/${YOUR_GITHUB_USERNAME}. It should have the following structure.

```shell
.
└── debs-flow
    ├── app.go
    ├── go.mod
    ├── go.sum
    └── sl.so
```

We will be focusing on the Handler function in app.go, which defines how we want the input stream to be processed.

Git clone the yomo-source-example repository. For clarity, we will call it debs-source.
```
$ git clone git@github.com:yomorun/yomo-source-example.git
```

Create a file named workflow.yaml with the following content. Place it under debs-source.

name: service
host: localhost
port: 9000
flows:
- name: debs 2014
    host: localhost
    port: 4242
sinks:
- name: mock db
    host: localhost
    port: 4243

Make sure the $GOPATH/src/github.com/${YOUR_GITHUB_USERNAME} directory contains the following files.

```shell
.
├── debs-flow
│   ├── app.go
│   ├── go.mod
│   ├── go.sum
│   └── sl.so
└── debs-source
    ├── go.mod
    ├── go.sum
    ├── main.go
    └── workflow.yaml
```

Algorithm Implementation

By default, the Handler function in debs-flow/app.go should look as follows.

func Handler(rxstream rx.RxStream) rx.RxStream {
    stream := rxstream.
        Subscribe(0x10).
        OnObserve(decoder).
        Map(printer).
        Encode(0x11)

    return stream
}

Subscribe(0x10): Subscribe to the input stream. 0x10 is the key. It is defined in debs-source/main.go.
OnObserve(decoder): Decode []byte to interface{}. Empty interfaces are often used by code that handles values of unknown type.
Map(printer): Print out the data.

For query #1, we need to define two functions in addition to what we discussed above. We will call them average and predict.

func Handler(rxstream rx.RxStream) rx.RxStream {
    stream := rxstream.
        Subscribe(0x10).
        OnObserve(decoder).
        Map(printer).
        BufferWithTime(ss * 1e3). // ss stands for slice size
        Map(average).
        Map(predict).
        Encode(0x11)

    return stream
}

// compute the average load for each plug and save the values to db, which is a global variable in this example
func average(_ context.Context, i interface{}) (interface{}, error) {
    // convert interface{} to []interface{}
    lst, ok := i.([]interface{})
    if !ok {
        err := fmt.Sprintf("expected type '[]interface{}', got '%v' instead",
            reflect.TypeOf(i))
        fmt.Printf("[average] %v\n", err)
        return nil, fmt.Errorf(err)
    }

    // plug # -> value
    total := make(map[string]float32)
    count := make(map[string]float32)

    for _, elem := range lst {
        // convert interface{} to measurement
        x, ok := elem.(Measurement)
        if !ok {
            err := fmt.Sprintf("expected type 'measurement', got '%v' instead",
                reflect.TypeOf(elem))
            fmt.Printf("[average] %v\n", err)
            return nil, fmt.Errorf(err)
        }

        if x.Property { // load
            plug := x.toString()
            total[plug] += x.Value
            count[plug] += 1.0
        }
    }

    // save to db
    fmt.Println("*** average ***")
    for plug, v := range total {
        avg := v / count[plug]
        fmt.Printf("[s_%v] %v %v\n", idx, plug, avg)

        _, ok := db[plug]
        if !ok {
            db[plug] = make(map[uint32]float32)
        }
        db[plug][idx] = avg
    }
    fmt.Println("***************")
    return i, nil
}

// make predictions based on what we have in db
func predict(_ context.Context, i interface{}) (interface{}, error) {
    k := t / ss

    fmt.Println("*** predict ***")
    l := (idx + 2) / k
    if l == 0 {
        fmt.Println("not enough data")
    } else {
        // possible values for j
        lst := make([]uint32, l)
        for m := range lst {
            n := uint32(m + 1)
            j := idx + 2 - n*k
            lst[m] = j
        }

        for plug := range db {
            // average load for s_j
            data := make([]float32, l)
            for m, j := range lst {
                data[m] = db[plug][j]
            }
            pred := (db[plug][idx] + median(data)) / 2
            fmt.Printf("[s_%v] %v %v\n", idx+2, plug, pred)
        }
    }
    fmt.Println("***************")

    idx += 1 // slice #
    return 0.0, nil
}

For query #2, we will define a function called outliers.

func Handler(rxstream rx.RxStream) rx.RxStream {
    stream := rxstream.
        Subscribe(0x10).
        OnObserve(decoder).
        Map(printer).
        BufferWithTime(ss * 1e3).
        Map(outliers).
        Encode(0x11)

    return stream
}

// which plugs have a median load greater than the median load of all plugs?
func outliers(_ context.Context, i interface{}) (interface{}, error) {
    // convert interface{} to []interface{}
    lst, ok := i.([]interface{})
    if !ok {
        err := fmt.Sprintf("expected type '[]interface{}', got '%v' instead",
            reflect.TypeOf(i))
        fmt.Printf("[outliers] %v\n", err)
        return nil, fmt.Errorf(err)
    }

    all := make([]float32, 0, len(lst))
    indiv := make(map[string][]float32) // plug # -> values

    for _, elem := range lst {
        // convert interface{} to measurement
        x, ok := elem.(Measurement)
        if !ok {
            err := fmt.Sprintf("expected type 'measurement', got '%v' instead",
                reflect.TypeOf(elem))
            fmt.Printf("[outliers] %v\n", err)
            return nil, fmt.Errorf(err)
        }

        if x.Property { // load
            all = append(all, x.Value)

            plug := x.toString()
            indiv[plug] = append(indiv[plug], x.Value)
        }
    }

    v := median(all)
    fmt.Printf("all plugs: %v\n", v)

    fmt.Println("*** outliers ***")
    for plug, vs := range indiv {
        m := median(vs)
        if m > v {
            fmt.Printf("[w_%v] %v %v\n", idx, plug, m)
        }
    }
    fmt.Println("****************")

    idx += 1
    return 0.0, nil
}

Now to run the code, we need to:

cd into debs-flow and type
```
$ yomo run app.go
```

cd into debs-source and type

$ yomo wf run workflow.yaml
$ PORT=9000 go run main.go

You might want to try a different set of hyperparameters.

Results

For query #1, you should see something similar to the following.

...
[1620461050] 9.910085 0-1-2 load
[1620461050] 8.087268 0-1-2 work
[1620461050] 13.468374 3-1-2 load
[1620461050] 7.742124 3-1-2 work
[1620461051] 13.738256 0-1-2 load
[1620461051] 16.59261 0-1-2 work
[1620461051] 12.84997 3-1-2 load
[1620461051] 10.838872 3-1-2 work
*** average ***
[s_18] 0-1-2 11.824171
[s_18] 3-1-2 13.159172
***************
*** predict ***
[s_20] 0-1-2 10.375039
[s_20] 3-1-2 12.0406475
***************
...

For query #2, something like this:

...
[1620461271] 6.921172 0-1-2 load
[1620461271] 1.8683584 0-1-2 work
[1620461271] 17.251171 3-1-2 load
[1620461271] 9.761936 3-1-2 work
[1620461272] 10.758014 0-1-2 load
[1620461272] 18.668419 0-1-2 work
[1620461272] 5.806175 3-1-2 load
[1620461272] 1.8562717 3-1-2 work
[1620461273] 0.11624338 0-1-2 load
[1620461273] 5.579194 0-1-2 work
[1620461273] 17.249205 3-1-2 load
[1620461273] 4.9580107 3-1-2 work
[1620461274] 8.087428 0-1-2 load
[1620461274] 7.49426 0-1-2 work
[1620461274] 4.6709924 3-1-2 load
[1620461274] 1.793222 3-1-2 work
[1620461275] 4.8114495 0-1-2 load
[1620461275] 1.9070174 0-1-2 work
[1620461275] 19.199306 3-1-2 load
[1620461275] 9.054778 3-1-2 work
all plugs: 7.5043
*** outliers ***
[s_5] 3-1-2 17.249205
****************
...

About Author

Ivy Guo is a Computer Science student at the University of Washington. If you have any questions, please email Ivy at zhifeig@cs.washington.edu

References

Zbigniew Jerzak and Holger Ziekow. 2014. The DEBS 2014 grand challenge. In Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems (DEBS '14). Association for Computing Machinery, New York, NY, USA, 266–269. DOI: https://doi.org/10.1145/2611286.2611333.

More about YoMo

https://github.com/yomorun/yomo